A first estimate of the structure and density of the populations of pet cats and dogs across Great Britain.
A first estimate of the structure and density of the populations of pet cats and dogs across Great Britain.
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DOI：
10.1371/journal.pone.0174709
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发表时间：
2017
期刊：
影响因子：
3.7
通讯作者：
Smith GC
中科院分区：
文献类型：
--
作者：
Aegerter J;Fouracre D;Smith GC
政策的制定、实施和有效的应急反应依赖于强有力的证据基础，以确保成功和成本效益。如果这包括防止感染伴侣猫和狗的人畜共患病或兽医疾病的建立或传播，则描述这些宠物的种群结构和密度是有用的。同样，这样的描述可能有助于支持不同的研究领域，如；循证兽医实践，兽医流行病学，公共卫生和生态学。除了绘制宠物所在位置的地图外，估计兽医很少或永远不会看到多少宠物，以及在疾病爆发时可能无法得到适当管理，这一点也很重要。不幸的是，这两个证据来源都没有出现在科学和监管文献中。我们对宠物种群的结构和密度进行了首次估计，方法是使用英国最新的全国猫狗种群估计数据，并将这些数据在空间上进行了细分，并按所有权类别进行了分类。对于空间模型，我们使用英国各地兽医诊所的位置和规模来预测当地宠物密度，使用客户旅行时间来定义诊所周围的集水区，并将其与居住地址数据相结合来估计宠物拥有量。为了估计可能引发兽医或人畜共患疾病管理问题的宠物，我们回顾了文献并定义了一套全面的猫和狗的所有权类别，整理了每个所有权类别的亚种群及其相互作用率的估计，并对国家人口结构进行了连贯的规模描述。预测的宠物密度变化很大，农村地区密度最低，大城市中心密度最高，每个物种可超过2500只。相反，每户宠物的数量显示出相反的关系。定性和定量验证都支持模型结构中的关键假设，并表明该模型在预测地理尺度上的猫种群方面是有用的，这对决策很重要，尽管它也指出了进一步研究可以改进模型性能的地方。在发生动物健康危机时，似乎几乎所有的狗都能迅速得到控制。对于猫来说，大量数量未知的猫可能永远无法在控制下购买，并且不太可能获得兽医支持，以促进监测和疾病管理；我们估计至少有150万只猫。此外，福利机构缺乏多余的能力来照顾无主的猫，这表明，在危机时期，任何购买猫的比率的增加，或任何重新安置猫的比率的下降，都可能引发问题。
Policy development, implementation, and effective contingency response rely on a strong evidence base to ensure success and cost-effectiveness. Where this includes preventing the establishment or spread of zoonotic or veterinary diseases infecting companion cats and dogs, descriptions of the structure and density of the populations of these pets are useful. Similarly, such descriptions may help in supporting diverse fields of study such as; evidence-based veterinary practice, veterinary epidemiology, public health and ecology. As well as maps of where pets are, estimates of how many may rarely, or never, be seen by veterinarians and might not be appropriately managed in the event of a disease outbreak are also important. Unfortunately both sources of evidence are absent from the scientific and regulatory literatures. We make this first estimate of the structure and density of pet populations by using the most recent national population estimates of cats and dogs across Great Britain and subdividing these spatially, and categorically across ownership classes. For the spatial model we used the location and size of veterinary practises across GB to predict the local density of pets, using client travel time to define catchments around practises, and combined this with residential address data to estimate the rate of ownership. For the estimates of pets which may provoke problems in managing a veterinary or zoonotic disease we reviewed the literature and defined a comprehensive suite of ownership classes for cats and dogs, collated estimates of the sub-populations for each ownership class as well as their rates of interaction and produced a coherent scaled description of the structure of the national population. The predicted density of pets varied substantially, with the lowest densities in rural areas, and the highest in the centres of large cities where each species could exceed 2500 animals.km-2. Conversely, the number of pets per household showed the opposite relationship. Both qualitative and quantitative validation support key assumptions in the model structure and suggest the model is useful at predicting the populations of cats at geographical scales important for decision-making, although it also indicates where further research may improve model performance. In the event of an animal health crisis, it appears that almost all dogs could be brought under control rapidly. For cats, a substantial and unknown number might never be bought under control and would be less likely to receive veterinary support to facilitate surveillance and disease management; we estimate this to be at least 1.5 million cats. In addition, the lack of spare capacity to care for unowned cats in welfare organisations suggests that any increase in their rate of acquisition of cats, or any decrease in the rate of re-homing might provoke problems during a period of crisis.